Affiliations: School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, China
Correspondence:
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Corresponding author. Linhui Li, School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, 116024, China. E-mail: lilinhui@dlut.edu.cn.
Note: [1] This work was supported by the National Natural Science Foundation of China (Grant Nos. 51775082, 61976039) and the China Fundamental Research Funds for the Central Universities (Grant Nos. DUT19LAB36, DUT20GJ207), and Science and Technology Innovation Fund of Dalian (2018J12GX061).
Abstract: Object detection and tracking are critical and challenging problems in vehicle environment perception systems, and have received broad attention in recent years. A novel detection and tracking algorithm taking both accuracy and real-time performance into account is proposed in this paper. First, we employ a fusion algorithm based on stereo vision and deep learning in object detection, which achieves high accuracy using two complementary algorithms. Then, a prediction-association algorithm which uses a Kalman filter and Hungarian assignment for multiple object tracking is employed for object tracking. In addition, a detection and tracking framework based on stereo vision improves the robustness of environmental perception system. Experimental results demonstrate that the proposed algorithm has high accuracy and can meet the real-time performance requirement.
Keywords: Environmental perception, stereo vision, deep network, multiple object tracking
